clearml/examples/jupyter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAINS Task: created new task id=e8fc2b809a384c3f8ec3ded54a2aae44\n",
      "TRAINS results page: http://ec2-3-218-72-191.compute-1.amazonaws.com:8080/projects/ec4476fb59c64d89af880ff0445c836b/experiments/e8fc2b809a384c3f8ec3ded54a2aae44/output/log\n"
     ]
    }
   ],
   "source": [
    "from trains import Task\n",
    "task = Task.init(project_name='examples', task_name='Jupyter exmaple')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcd6491d898>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 50\n",
    "x = np.random.rand(N)\n",
    "y = np.random.rand(N)\n",
    "colors = np.random.rand(N)\n",
    "area = (30 * np.random.rand(N))**2  # 0 to 15 point radii\n",
    "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n",
    "plt.show()\n",
    "\n",
    "x = np.linspace(0, 10, 30)\n",
    "y = np.sin(x)\n",
    "plt.plot(x, y, 'o', color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fcd646ff550>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = np.eye(8, 8, dtype=np.uint8)\n",
    "plt.imshow(m)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PyCharm (trains-internal)",
   "language": "python",
   "name": "pycharm-40126efe"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}